What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Linear programming
His primary scientific interests are in Mathematical optimization, Simulation, Algorithm, Genetic algorithm and Search algorithm.
The concepts of his Mathematical optimization study are interwoven with issues in Fuzzy set operations and Fuzzy transportation.
His Simulation study combines topics in areas such as Iterative method and Job shop scheduling.
His biological study focuses on Tabu search.
His Branch and bound research also works with subjects such as
- Goal programming most often made with reference to Algorithm design,
- Lagrange multiplier together with Lagrangian relaxation and Integer programming.
He interconnects Decision variables, Collaborative optimization, Solver and Minification in the investigation of issues within Scheduling.
His most cited work include:
- Optimizing trains movement on a railway network (130 citations)
- Fuzzy fixed charge solid transportation problem and algorithm (126 citations)
- Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach (124 citations)
What are the main themes of his work throughout his whole career to date?
Lixing Yang mainly investigates Mathematical optimization, Control theory, Integer programming, Simulation and Schedule.
As a member of one scientific family, Lixing Yang mostly works in the field of Mathematical optimization, focusing on Path and, on occasion, Hybrid algorithm.
His Control theory study combines topics from a wide range of disciplines, such as Control engineering, Model predictive control and Headway.
His research investigates the connection between Headway and topics such as Urban rail transit that intersect with issues in Dynamic programming.
As part of one scientific family, Lixing Yang deals mainly with the area of Integer programming, narrowing it down to issues related to the Stochastic optimization, and often Stochastic programming.
Lixing Yang has included themes like Maintenance planning, Automotive engineering and Minification in his Scheduling study.
He most often published in these fields:
- Mathematical optimization (50.00%)
- Control theory (16.36%)
- Integer programming (15.45%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (50.00%)
- Integer programming (15.45%)
- Lagrangian relaxation (11.82%)
In recent papers he was focusing on the following fields of study:
Mathematical optimization, Integer programming, Lagrangian relaxation, Control theory and Headway are his primary areas of study.
In his study, Travel time is inextricably linked to Robustness, which falls within the broad field of Mathematical optimization.
His Integer programming study deals with Stochastic optimization intersecting with Urban rail transit.
His Control theory research is multidisciplinary, incorporating elements of Perimeter control and Model predictive control.
His work is dedicated to discovering how Model predictive control, Linearization are connected with Stability and other disciplines.
The various areas that he examines in his Scheduling study include Maintenance plan, Maintenance planning, Task and Dynamic priority scheduling.
Between 2019 and 2021, his most popular works were:
- Collaborative optimization for metro train scheduling and train connections combined with passenger flow control strategy (27 citations)
- Energy-Efficient Subway Train Scheduling Design With Time-Dependent Demand Based on an Approximate Dynamic Programming Approach (13 citations)
- Joint optimization of train scheduling and maintenance planning in a railway network: A heuristic algorithm using Lagrangian relaxation (11 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Algorithm
- Linear programming
Lixing Yang focuses on Mathematical optimization, Integer programming, Lagrangian relaxation, Headway and Nonlinear mixed integer programming.
His work on Scheduling as part of general Mathematical optimization research is often related to Joint, thus linking different fields of science.
His biological study spans a wide range of topics, including Robust optimization and Travel time.
His research in Lagrangian relaxation intersects with topics in Urban rail transit, Lagrange multiplier and Linear programming.
His Headway study integrates concerns from other disciplines, such as Message passing, Model predictive control and Control theory, Optimal control, Electronic speed control.
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